HEAD-TOE DISTANCE AS A SIMPLE MEASURE TO EVALUATE AMPLITUDE OF CIRCLES ON POMMEL HORSE
Bibliographic record
Abstract
To develop scientifically-valid tools to monitor performance in practice, a critical question is what to measure. On pommel horse, the importance of fundamental skills called circles is uncontroversial, and one of the key performance qualities of circles is the amplitude of the movement. Previous studies have used joint angles or the magnitude of a body part’s trajectory to evaluate the amplitude, but we hypothesized that the distance between two points, namely a head and toes might be substituted despite its relative simplicity. This study examined the use of Head-Toe Distance (HTD) normalized by the gymnast’s body height as a simple variable to potentially evaluate the amplitude of circles. The kinematic data of circles performed by 18 elite gymnasts were collected with a Qualisys motion capture system operating at 100 Hz. HTD and its horizontal component, HTDh, were computed along with their relationships to the outcome scores given by the official judges, as well as the other amplitude variables: the horizontal diameters of shoulder and ankle trajectories; the body flexion angle; and in the rear support position, the shoulder extension angle and the head position. The results supported HTDh, rather than HTD, for its potential usage as a single variable to evaluate the amplitude of circles. The benefits of HTDh compared to the other variables lies in its potential validity despite its relative simplicity in assessment. Because computing HTDh requires only the positional data of the head and toes, it may have greater practical applications as an evaluative tool in gymnastics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".